Understanding Communication Patterns in HPCG

Conjugate Gradient (CG) algorithms form a large part of many HPC applications, examples include bioinformatics and weather applications. These algorithms allow numerical solutions to complex linear systems. Understanding how distributed implementations of these algorithms use a network interconnect will allow system designers to gain a deeper insight into their exacting requirements for existing and future applications. This short paper documents our initial investigation into the communication patterns present in the High Performance Conjugate Gradient (HPCG) benchmark. Through our analysis, we identify patterns and features which may warrant further investigation to improve the performance of CG algorithms and applications which make extensive use of them. In this paper, we capture communication traces from runs of the HPCG benchmark at a variety of different processor counts and then examine this data to identify potential performance bottlenecks. Initial results show that there is a fall in the throughput of the network when more processes are communicating with each other, due to network contention.